A bioelectric or biomagnetic signal measured from the forehead of a subject includes different signal components, originating from physiological activities of brain, eyeballs and facial muscles, for example. The study of electromagnetic activity generated by brain has a significant role in physiological and clinical settings. The electrical component of the brain activity is called the electroencephalogram (EEG) and its magnetic counterpart the magnetoencephalogram (MEG). The EEG and the MEG have different sensitivities to sources of different orientations and locations but the primary currents causing the said signals are the same. Similarities between these waveforms are therefore to be expected.
Analogically, the electrical activity of muscles is called the electromyogram (EMG) and the ocular activity the electro-oculogram (EOG). The EMG and the EOG have their magnetic counterparts as well. However, they are not in practical use at the moment and are mainly considered as artifacts.
Ocular electromagnetic activity is mainly within a frequency range of 0 to 8 Hz, whereas the electromagnetic activity of the facial muscles is mainly at frequencies above 20 Hz. In this context, ocular activity refers to eye movements or eye blinks. Eye movements are movements of the eyeball. The eyeball may be modelled as an electrical dipole, because the retina is positively and the cornea negatively charged. Eye movements produce large electromagnetic fields measurable on the forehead, which attenuate proportionally to the square of the distance from the eyes. An eye blink, i.e. the temporary closure of the eyelid, generates an electromagnetic field due to the motion of the eyelid over the cornea. In electrical engineering terms, an eye blink thus means a short circuit caused by the closure of the eyelid.
Low-frequency brain activity lies within the same frequency band as the ocular activity. Below, ocular activity and low-frequency brain activitivity are discussed briefly.
The EOG is a time-varying signal, which includes asymmetrical wave forms in time-domain. Successive EOG waves do not follow each other immediately. In practice, this means that in a given time window the signal includes both periods containing EOG activity and periods not containing EOG activity. Therefore, the statistical properties of an EOG signal change in a given time window, and the signal can be said to be non-stationary. A further characteristic feature of the EOG is that periods between successive EOG waves are unpredictable. Therefore, the EOG may also be said to be non-periodical. Traditional use of the EOG is in sleep recordings.
Low-frequency brain activity refers to Delta and Theta rhythms. The Delta rhythms are commonly defined as the activity between 1 and 4 Hz. The Delta rhythms have two distinct origins: one is in the cortex and the other in the thalamus. The Theta rhythms are usually considered as the activity within the frequency range of 4 to 7 Hz. Both the Delta and Theta rhythms are rare in a healthy, awake adult. However, they arise during sleep or drug-induced anesthesia or sedation.
Low-frequency brain activity is periodical in nature. A single wave of low-frequency brain activity of a healthy person is symmetrical in time-domain. Additionally, brain activity is stationary, since successive brain waves typically follow each other immediately. The above-mentioned features lead to the fact that the brain activity of a sleeping or anesthetized person includes distinct peaks below 10 Hz, called the dominant frequencies.
The above-described signals may be used in various ways to assess the state of a subject. This is discussed briefly in the following.
The EEG is a well-established method for assessing brain activity by recording and analyzing the weak biopotential signals generated in the cortex of the brain with electrodes attached on the skin of the skull. The EEG has been in wide use for decades in basic research of the neural systems of the brain, as well as in clinical diagnosis of various neurophysiological diseases and disorders. During the past few years, several commercial devices for measuring the level of consciousness and/or awareness in a clinical set-up during anesthesia have become available. These devices, which have been introduced by Aspect Medical Systems (Bispectral Index) and Datex-Ohmeda (Entropy™), for example, describe EEG characteristics as a single number indicative of the said level.
A signal from an awake or lightly sedated subject includes eye movements and blinks, which disappear before the surgical level of anesthesia is reached. The EEG activity of a healthy awake patient concentrates mainly on higher frequencies, whereas in deepening anesthesia the activity becomes slower and low-frequency EEG starts to dominate. Correct classification of the ocular and low-frequency EEG activities becomes therefore an important issue for the recognition of a wake state and the states of anesthesia or sedation. This is especially important at the conduction of anesthesia, where the transition from a conscious to an unconscious state takes place quickly. The patient often moves his/her eyes even just before unconsciousness is reached. This is illustrated in FIG. 1, which shows an EEG signal measured when the patient is about to reach an unconscious state. The peaks denoted with reference number 10 originate from eye movements. These peaks disappear when unconsciousness is reached.
It is thus difficult to track the change of the state of the patient. However, if the change cannot be detected quickly, the exact time of reaching unconsciousness is impossible to determine.
For these purposes, a technique based on the concept of near-field or far-field potentials may be used, as described in the U.S. Pat. No. 6,032,072. In practice, this technique requires at least two channels to be measured, the first channel representing the near-field potential and the second channel the far-field potential.
The above-described signals may also be used in sleep studies. Polygraphic recording of sleep typically includes monitoring of EEG, ECG (electrocardiogram), EOG, EMG, and respiration signals. At least a single channel EEG and a single channel EOG measurement is then required. The EOG electrodes are connected to the corners of both eyes, vertically at different levels. Based on the EEG and EOG characteristics, sleep is normally categorized into six different levels: awake, S1, S2, S3, S4, and REM (Rapid Eye Movement). Eye movements are most prominent at the awake and REM levels. REM periods are identified based on the existence of saccadic eye movements. Low-frequency EEG appears commonly at the levels S2, S3 and S4. For the correct classification, it is important to distinguish EOG from frontal Delta activity. Traditionally, piezoelectric sensors are connected to the eyelid to identify the eye movements.
A further application of the above-mentioned signals is the monitoring of the state of alertness, which has a number of clinical applications. By means of these systems, shift workers, truck drivers, train operators, and other individuals who work during hours of maximum sleepiness may be notified when they become too drowsy. Both EEG and EOG analysis may be used for defining the level of alertness. In an alert subject, the eye movements are fast, whereas in a lowered state of alertness the eye movements become slower. Fatigue, drugs and alcohol, for example, slow down saccadic eye movements. There is a growing evidence indicating that sleep loss and associated decrements in neurobehavioral function are reflected in the spectral composition of the EEG during wakefulness as well as in the incidence of slow eye movements recorded by the EOG. The incidence of slow eye movements during wakefulness increases during periods of sleep loss and correlates with changes in alertness and psychomotor vigilance.
Spectral entropy derived from the frequency range of the EOG may be utilized for monitoring alertness. The spectrum of saccadic eye movements lies principally at higher frequencies than the spectrum of slow eye movements. Additionally, the wave form of saccadic eye movements includes more rapid changes than that of slow eye movements, being therefore less similar to sine wave than the waveform of slow eye movements. As a result, the spectral entropy of saccadic eye movements is higher than that of slow eye movements.
As discussed above, correct identification of ocular and low-frequency brain activity is important in many monitoring or control systems, such as in sleep diagnosis and in assessment of the depth of anesthesia or sedation. However, these two activities cannot be identified based on the spectral power since the frequency bands of the said two activities are overlapping and since the spectral powers of biosignals are known to be subjective. To illustrate the problem, FIG. 2 shows an example of the spectra of two signals. In the figure, the continuous line represents the power spectrum of a signal including EEG, EOG, and EMG components, while the dashed line represents the power spectrum of a pure EEG signal. As can be seen, the spectra are very much alike at low frequencies.
The correct identification of the said two activities is therefore complicated. As discussed above, the identification normally requires at least two measurements signals, one for each signal component of interest, coupled with complicated signal processing.
The present invention seeks to alleviate the above problems related to the detection and separation of the EEG and EOG signals and to bring about a method by means of which the fidelity of the EEG and/or the EOG signals may be improved in an uncomplicated way using only a single measurement signal obtained from the subject.